Overview

Dataset statistics

Number of variables10
Number of observations28284
Missing cells22953
Missing cells (%)8.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory88.0 B

Variable types

Numeric10

Alerts

EC is highly overall correlated with DOHigh correlation
DO is highly overall correlated with EC and 1 other fieldsHigh correlation
Temp is highly overall correlated with DOHigh correlation
pH has 3779 (13.4%) missing valuesMissing
EC has 7560 (26.7%) missing valuesMissing
DO has 3805 (13.5%) missing valuesMissing
TP has 385 (1.4%) missing valuesMissing
ORP has 3705 (13.1%) missing valuesMissing
Temp has 3703 (13.1%) missing valuesMissing
TEMP has unique valuesUnique

Reproduction

Analysis started2023-03-05 04:37:55.763985
Analysis finished2023-03-05 04:38:15.322144
Duration19.56 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

pH
Real number (ℝ)

Distinct8492
Distinct (%)34.7%
Missing3779
Missing (%)13.4%
Infinite0
Infinite (%)0.0%
Mean7.3769509
Minimum0.19
Maximum12.9725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.9 KiB
2023-03-05T11:38:15.532334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile5.0608333
Q16.3316667
median7.1183333
Q37.9141667
95-th percentile11.548333
Maximum12.9725
Range12.7825
Interquartile range (IQR)1.5825

Descriptive statistics

Standard deviation1.8605702
Coefficient of variation (CV)0.25221399
Kurtosis1.1393273
Mean7.3769509
Median Absolute Deviation (MAD)0.79041667
Skewness0.6973521
Sum180772.18
Variance3.4617215
MonotonicityNot monotonic
2023-03-05T11:38:15.740167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.78 26
 
0.1%
6.79 23
 
0.1%
5.62 20
 
0.1%
6.84 20
 
0.1%
6.2 19
 
0.1%
6.77 19
 
0.1%
7.5075 17
 
0.1%
7.238333333 16
 
0.1%
7.32 16
 
0.1%
7.284166667 16
 
0.1%
Other values (8482) 24313
86.0%
(Missing) 3779
 
13.4%
ValueCountFrequency (%)
0.19 1
 
< 0.1%
0.21 3
< 0.1%
0.24 1
 
< 0.1%
0.505 1
 
< 0.1%
0.9485714286 1
 
< 0.1%
1.01 1
 
< 0.1%
1.015 1
 
< 0.1%
1.710833333 1
 
< 0.1%
1.773333333 1
 
< 0.1%
1.794166667 1
 
< 0.1%
ValueCountFrequency (%)
12.9725 1
< 0.1%
12.91583333 1
< 0.1%
12.906 1
< 0.1%
12.89583333 1
< 0.1%
12.88333333 1
< 0.1%
12.84916667 1
< 0.1%
12.82583333 1
< 0.1%
12.81416667 1
< 0.1%
12.80916667 1
< 0.1%
12.8025 1
< 0.1%

EC
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19001
Distinct (%)91.7%
Missing7560
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean24833.394
Minimum0.01
Maximum49986.975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.9 KiB
2023-03-05T11:38:15.937318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile8.6051667
Q12992.5004
median29506.673
Q341737.033
95-th percentile46482.286
Maximum49986.975
Range49986.965
Interquartile range (IQR)38744.533

Descriptive statistics

Standard deviation18298.128
Coefficient of variation (CV)0.73683559
Kurtosis-1.6020623
Mean24833.394
Median Absolute Deviation (MAD)14776.052
Skewness-0.29067802
Sum5.1464726 × 108
Variance3.348215 × 108
MonotonicityNot monotonic
2023-03-05T11:38:16.142249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 308
 
1.1%
9.38 145
 
0.5%
6.25 106
 
0.4%
15.62 85
 
0.3%
3.13 33
 
0.1%
12.5 24
 
0.1%
18.75 23
 
0.1%
6.4675 13
 
< 0.1%
9.336666667 13
 
< 0.1%
9.9 13
 
< 0.1%
Other values (18991) 19961
70.6%
(Missing) 7560
 
26.7%
ValueCountFrequency (%)
0.01 3
 
< 0.1%
0.11 1
 
< 0.1%
0.52 9
< 0.1%
0.53 2
 
< 0.1%
0.63 2
 
< 0.1%
0.732 1
 
< 0.1%
0.78 5
< 0.1%
0.7825 1
 
< 0.1%
0.785 1
 
< 0.1%
0.79 1
 
< 0.1%
ValueCountFrequency (%)
49986.975 1
< 0.1%
49968.575 1
< 0.1%
49963.4125 1
< 0.1%
49962.3075 1
< 0.1%
49961.46 1
< 0.1%
49942.9675 1
< 0.1%
49935.04571 1
< 0.1%
49929.755 1
< 0.1%
49924.05273 1
< 0.1%
49913.935 1
< 0.1%

DO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9006
Distinct (%)36.8%
Missing3805
Missing (%)13.5%
Infinite0
Infinite (%)0.0%
Mean5.8992065
Minimum0.01
Maximum10.785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.9 KiB
2023-03-05T11:38:16.332109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile2.1524167
Q14.1427273
median6.7425
Q37.5366667
95-th percentile8.2841667
Maximum10.785
Range10.775
Interquartile range (IQR)3.3939394

Descriptive statistics

Standard deviation2.0652889
Coefficient of variation (CV)0.35009605
Kurtosis-0.43297659
Mean5.8992065
Median Absolute Deviation (MAD)1.2508333
Skewness-0.74785902
Sum144406.68
Variance4.2654183
MonotonicityNot monotonic
2023-03-05T11:38:16.599876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 170
 
0.6%
0.565 56
 
0.2%
0.2875 23
 
0.1%
7.066666667 16
 
0.1%
0.015 16
 
0.1%
7.1175 16
 
0.1%
7.116666667 16
 
0.1%
7.123333333 15
 
0.1%
6.9675 14
 
< 0.1%
7.3 14
 
< 0.1%
Other values (8996) 24123
85.3%
(Missing) 3805
 
13.5%
ValueCountFrequency (%)
0.01 170
0.6%
0.01083333333 2
 
< 0.1%
0.01111111111 1
 
< 0.1%
0.01166666667 1
 
< 0.1%
0.01363636364 1
 
< 0.1%
0.01416666667 1
 
< 0.1%
0.015 16
 
0.1%
0.02 3
 
< 0.1%
0.02090909091 1
 
< 0.1%
0.025 1
 
< 0.1%
ValueCountFrequency (%)
10.785 1
< 0.1%
9.484166667 1
< 0.1%
9.361666667 1
< 0.1%
9.2225 1
< 0.1%
9.181818182 1
< 0.1%
9.1475 1
< 0.1%
9.145833333 1
< 0.1%
9.104166667 1
< 0.1%
9.060909091 1
< 0.1%
9.055833333 1
< 0.1%

TSS
Real number (ℝ)

Distinct26720
Distinct (%)94.5%
Missing14
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean105.50781
Minimum0.0125
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.9 KiB
2023-03-05T11:38:16.797425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0125
5-th percentile1.6462083
Q130.331042
median109.56744
Q3146.33029
95-th percentile191.72383
Maximum999
Range998.9875
Interquartile range (IQR)115.99925

Descriptive statistics

Standard deviation109.19446
Coefficient of variation (CV)1.034942
Kurtosis33.103924
Mean105.50781
Median Absolute Deviation (MAD)49.814014
Skewness4.6092594
Sum2982705.7
Variance11923.43
MonotonicityNot monotonic
2023-03-05T11:38:17.065696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999 208
 
0.7%
1.44 94
 
0.3%
1.38 65
 
0.2%
1.06 63
 
0.2%
1.56 55
 
0.2%
1.12 32
 
0.1%
1.25 22
 
0.1%
0.94 20
 
0.1%
1.19 18
 
0.1%
1.62 15
 
0.1%
Other values (26710) 27678
97.9%
ValueCountFrequency (%)
0.0125 1
< 0.1%
0.02 1
< 0.1%
0.02272727273 1
< 0.1%
0.03142857143 1
< 0.1%
0.04444444444 1
< 0.1%
0.04636363636 1
< 0.1%
0.05 1
< 0.1%
0.05555555556 1
< 0.1%
0.05636363636 1
< 0.1%
0.05727272727 1
< 0.1%
ValueCountFrequency (%)
999 208
0.7%
926.3294814 1
 
< 0.1%
924.3255759 1
 
< 0.1%
854.9317504 1
 
< 0.1%
612.172313 1
 
< 0.1%
541.5766667 1
 
< 0.1%
541.5741667 1
 
< 0.1%
541.5733333 1
 
< 0.1%
541.5616667 1
 
< 0.1%
541.4533333 1
 
< 0.1%

TN
Real number (ℝ)

Distinct28165
Distinct (%)99.6%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.6018018
Minimum1.8077232
Maximum13.765242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.9 KiB
2023-03-05T11:38:17.296062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.8077232
5-th percentile2.7729578
Q13.2552725
median3.5886533
Q33.9300946
95-th percentile4.4480167
Maximum13.765242
Range11.957519
Interquartile range (IQR)0.67482207

Descriptive statistics

Standard deviation0.52666907
Coefficient of variation (CV)0.14622378
Kurtosis9.7495074
Mean3.6018018
Median Absolute Deviation (MAD)0.33698284
Skewness0.92566443
Sum101866.16
Variance0.27738031
MonotonicityNot monotonic
2023-03-05T11:38:17.556539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6 6
 
< 0.1%
3.51 6
 
< 0.1%
3.27 5
 
< 0.1%
3.5 4
 
< 0.1%
3.44 4
 
< 0.1%
3.5325 4
 
< 0.1%
3.25 4
 
< 0.1%
3.3 4
 
< 0.1%
3.46 4
 
< 0.1%
3.05 3
 
< 0.1%
Other values (28155) 28238
99.8%
ValueCountFrequency (%)
1.807723154 1
< 0.1%
1.853373451 1
< 0.1%
1.867351018 1
< 0.1%
1.923949488 1
< 0.1%
1.943381525 1
< 0.1%
1.956194734 1
< 0.1%
1.960753482 1
< 0.1%
1.963886383 1
< 0.1%
1.983760958 1
< 0.1%
2.025833333 1
< 0.1%
ValueCountFrequency (%)
13.76524247 1
< 0.1%
10 1
< 0.1%
9.729166667 1
< 0.1%
9.5325 1
< 0.1%
9.005 1
< 0.1%
8.991666667 1
< 0.1%
8.74 1
< 0.1%
8.73 1
< 0.1%
8.219166667 1
< 0.1%
7.944515481 1
< 0.1%

TP
Real number (ℝ)

Distinct27718
Distinct (%)99.4%
Missing385
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean73.445576
Minimum0.01
Maximum224.57753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.9 KiB
2023-03-05T11:38:17.768208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile47.68483
Q162.536525
median73.166216
Q384.517182
95-th percentile102.21543
Maximum224.57753
Range224.56753
Interquartile range (IQR)21.980656

Descriptive statistics

Standard deviation17.768747
Coefficient of variation (CV)0.2419308
Kurtosis2.2923111
Mean73.445576
Median Absolute Deviation (MAD)10.98173
Skewness-0.33775318
Sum2049058.1
Variance315.72835
MonotonicityNot monotonic
2023-03-05T11:38:17.975820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 53
 
0.2%
0.02 38
 
0.1%
0.03 16
 
0.1%
0.05 12
 
< 0.1%
0.04 9
 
< 0.1%
0.07 7
 
< 0.1%
0.11 6
 
< 0.1%
0.0175 5
 
< 0.1%
0.1 5
 
< 0.1%
0.0225 5
 
< 0.1%
Other values (27708) 27743
98.1%
(Missing) 385
 
1.4%
ValueCountFrequency (%)
0.01 53
0.2%
0.01083333333 2
 
< 0.1%
0.0125 3
 
< 0.1%
0.01333333333 4
 
< 0.1%
0.01416666667 1
 
< 0.1%
0.01416666667 1
 
< 0.1%
0.015 4
 
< 0.1%
0.01666666667 2
 
< 0.1%
0.0175 5
 
< 0.1%
0.0175 1
 
< 0.1%
ValueCountFrequency (%)
224.577528 1
< 0.1%
188.4112099 1
< 0.1%
160.2055737 1
< 0.1%
151.3171901 1
< 0.1%
149.2781873 1
< 0.1%
148.2213012 1
< 0.1%
144.188259 1
< 0.1%
143.4504611 1
< 0.1%
142.5354201 1
< 0.1%
142.0147998 1
< 0.1%

TOC
Real number (ℝ)

Distinct28280
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.695868
Minimum1.2203431
Maximum28.698737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.9 KiB
2023-03-05T11:38:18.193426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.2203431
5-th percentile14.204185
Q116.849845
median18.70139
Q320.543824
95-th percentile23.176755
Maximum28.698737
Range27.478394
Interquartile range (IQR)3.6939792

Descriptive statistics

Standard deviation2.7283596
Coefficient of variation (CV)0.14593383
Kurtosis0.33948026
Mean18.695868
Median Absolute Deviation (MAD)1.8457
Skewness-0.069435048
Sum528793.93
Variance7.4439461
MonotonicityNot monotonic
2023-03-05T11:38:18.425151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.05 5
 
< 0.1%
20.29831044 1
 
< 0.1%
21.40274803 1
 
< 0.1%
22.16273422 1
 
< 0.1%
18.34244791 1
 
< 0.1%
22.61328315 1
 
< 0.1%
20.11231348 1
 
< 0.1%
18.66205977 1
 
< 0.1%
16.59146932 1
 
< 0.1%
18.83737437 1
 
< 0.1%
Other values (28270) 28270
> 99.9%
ValueCountFrequency (%)
1.220343136 1
 
< 0.1%
2.05 5
< 0.1%
2.580055335 1
 
< 0.1%
2.703584956 1
 
< 0.1%
2.879453715 1
 
< 0.1%
3.734166667 1
 
< 0.1%
4.514903896 1
 
< 0.1%
5.666835791 1
 
< 0.1%
8.479058574 1
 
< 0.1%
8.690378616 1
 
< 0.1%
ValueCountFrequency (%)
28.698737 1
< 0.1%
28.69592048 1
< 0.1%
28.23124973 1
< 0.1%
28.16604319 1
< 0.1%
28.13370611 1
< 0.1%
28.13291406 1
< 0.1%
28.04087572 1
< 0.1%
27.94455644 1
< 0.1%
27.94139882 1
< 0.1%
27.87244333 1
< 0.1%

ORP
Real number (ℝ)

Distinct23578
Distinct (%)95.9%
Missing3705
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean366.09808
Minimum-653.81
Maximum1000
Zeros0
Zeros (%)0.0%
Negative845
Negative (%)3.0%
Memory size441.9 KiB
2023-03-05T11:38:18.701991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-653.81
5-th percentile72.582667
Q1205.32833
median343.825
Q3470.82208
95-th percentile960.04908
Maximum1000
Range1653.81
Interquartile range (IQR)265.49375

Descriptive statistics

Standard deviation266.34996
Coefficient of variation (CV)0.72753716
Kurtosis1.2282169
Mean366.09808
Median Absolute Deviation (MAD)135.3525
Skewness0.20307599
Sum8998324.7
Variance70942.301
MonotonicityNot monotonic
2023-03-05T11:38:18.915338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 641
 
2.3%
-442.87 4
 
< 0.1%
313.1675 3
 
< 0.1%
789.38 3
 
< 0.1%
379.8116667 3
 
< 0.1%
322.755 3
 
< 0.1%
237.7216667 3
 
< 0.1%
388.2966667 2
 
< 0.1%
387.7 2
 
< 0.1%
463.8458333 2
 
< 0.1%
Other values (23568) 23913
84.5%
(Missing) 3705
 
13.1%
ValueCountFrequency (%)
-653.81 1
< 0.1%
-506.2925 1
< 0.1%
-504.8891667 1
< 0.1%
-502.8716667 1
< 0.1%
-501.2783333 1
< 0.1%
-500 1
< 0.1%
-499.8725 1
< 0.1%
-493.94 1
< 0.1%
-492.8441667 1
< 0.1%
-491.5 1
< 0.1%
ValueCountFrequency (%)
1000 641
2.3%
999.9966667 1
 
< 0.1%
999.995 1
 
< 0.1%
999.9808333 1
 
< 0.1%
999.9783333 1
 
< 0.1%
999.9666667 1
 
< 0.1%
999.965 1
 
< 0.1%
999.9566667 1
 
< 0.1%
999.955 1
 
< 0.1%
999.9291667 1
 
< 0.1%

Temp
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14971
Distinct (%)60.9%
Missing3703
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean27.578277
Minimum3.61
Maximum45.901429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.9 KiB
2023-03-05T11:38:19.161224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.61
5-th percentile21.760833
Q124.392727
median27.245833
Q330.735
95-th percentile33.583333
Maximum45.901429
Range42.291429
Interquartile range (IQR)6.3422727

Descriptive statistics

Standard deviation3.8566355
Coefficient of variation (CV)0.13984324
Kurtosis-0.76477646
Mean27.578277
Median Absolute Deviation (MAD)3.1525
Skewness0.17759645
Sum677901.63
Variance14.873638
MonotonicityNot monotonic
2023-03-05T11:38:19.373993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.58416667 10
 
< 0.1%
30.29166667 10
 
< 0.1%
24.58 9
 
< 0.1%
23.54 9
 
< 0.1%
25.18 9
 
< 0.1%
26.32083333 9
 
< 0.1%
24.22 8
 
< 0.1%
23.94416667 8
 
< 0.1%
24.96 8
 
< 0.1%
24.95333333 8
 
< 0.1%
Other values (14961) 24493
86.6%
(Missing) 3703
 
13.1%
ValueCountFrequency (%)
3.61 1
< 0.1%
18.335 1
< 0.1%
19.07666667 1
< 0.1%
19.22166667 1
< 0.1%
19.33333333 1
< 0.1%
19.4375 1
< 0.1%
19.44833333 1
< 0.1%
19.55 1
< 0.1%
19.58666667 1
< 0.1%
19.58666667 1
< 0.1%
ValueCountFrequency (%)
45.90142857 1
< 0.1%
45.70625 1
< 0.1%
44.47875 1
< 0.1%
44.09833333 1
< 0.1%
43.7625 1
< 0.1%
42 1
< 0.1%
41.76818182 1
< 0.1%
41.30666667 1
< 0.1%
39.8275 1
< 0.1%
39.68285714 1
< 0.1%

TEMP
Real number (ℝ)

Distinct28284
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.892659
Minimum25.787527
Maximum38.210078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size441.9 KiB
2023-03-05T11:38:19.608797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum25.787527
5-th percentile28.088959
Q129.139201
median29.88586
Q330.642271
95-th percentile31.729862
Maximum38.210078
Range12.42255
Interquartile range (IQR)1.5030696

Descriptive statistics

Standard deviation1.1059593
Coefficient of variation (CV)0.03699769
Kurtosis0.022080352
Mean29.892659
Median Absolute Deviation (MAD)0.75200022
Skewness0.026578553
Sum845483.96
Variance1.223146
MonotonicityNot monotonic
2023-03-05T11:38:19.991158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.74394703 1
 
< 0.1%
29.87314034 1
 
< 0.1%
30.80619644 1
 
< 0.1%
30.49834073 1
 
< 0.1%
29.95049299 1
 
< 0.1%
29.33167708 1
 
< 0.1%
30.13896888 1
 
< 0.1%
30.27257884 1
 
< 0.1%
29.38377556 1
 
< 0.1%
30.32187478 1
 
< 0.1%
Other values (28274) 28274
> 99.9%
ValueCountFrequency (%)
25.78752748 1
< 0.1%
25.86631805 1
< 0.1%
25.99156241 1
< 0.1%
26.06329095 1
< 0.1%
26.12858551 1
< 0.1%
26.1655313 1
< 0.1%
26.17158781 1
< 0.1%
26.18754438 1
< 0.1%
26.29426241 1
< 0.1%
26.31718827 1
< 0.1%
ValueCountFrequency (%)
38.21007751 1
< 0.1%
34.01284018 1
< 0.1%
33.922902 1
< 0.1%
33.71985494 1
< 0.1%
33.6501105 1
< 0.1%
33.58803177 1
< 0.1%
33.58394841 1
< 0.1%
33.56820977 1
< 0.1%
33.48524318 1
< 0.1%
33.46611082 1
< 0.1%

Interactions

2023-03-05T11:38:12.393708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:56.661356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:58.406259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:00.141819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:02.050258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:04.028235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:05.803064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:07.389068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:09.007879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:10.702174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:12.677258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:56.899803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:58.619933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:00.321305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:02.247357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:04.190463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:06.006282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:07.541420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:09.173646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:10.848210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:12.932575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:57.082437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:58.796539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:00.512033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:02.451012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:04.343412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:06.168222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:07.697120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:09.333030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:11.001927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:13.130569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:57.262984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:58.986312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:00.696201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:02.675829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:04.495825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:06.296957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:07.896797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:09.484127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:11.160511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:13.290509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:57.444478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:59.147968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:00.891797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:02.906827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:04.651160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:06.454896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:08.061861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:09.621309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:11.314125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:13.475450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:57.611092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:59.323082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:01.113866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:03.110714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:04.834173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:06.600905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:08.233575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:09.788302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:11.508642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:13.663320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:57.765992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:59.475524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:01.279645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:03.273371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:04.984262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:06.750330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:08.387078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:09.950660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:11.677961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:13.821601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:57.921899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:59.657772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:01.454365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:03.451539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:05.150458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:06.914811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:08.546885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:10.113312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:11.859775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:14.011603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:58.075303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:59.815337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:01.641471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:03.651209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:05.422418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:07.074959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:08.702061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:10.293288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:12.031402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:14.176650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:58.221070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:37:59.972151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:01.826108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:03.836041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:05.597368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:07.218297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:08.842665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:10.532870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-05T11:38:12.208648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-03-05T11:38:20.182197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
pHECDOTSSTNTPTOCORPTempTEMP
pH1.0000.0360.1060.032-0.010-0.015-0.009-0.3100.0800.009
EC0.0361.000-0.505-0.0420.0130.0110.0030.0290.311-0.001
DO0.106-0.5051.0000.132-0.0130.0130.012-0.171-0.516-0.002
TSS0.032-0.0420.1321.0000.0050.0260.0020.046-0.1760.002
TN-0.0100.013-0.0130.0051.000-0.004-0.002-0.014-0.017-0.003
TP-0.0150.0110.0130.026-0.0041.000-0.000-0.013-0.0350.006
TOC-0.0090.0030.0120.002-0.002-0.0001.000-0.008-0.011-0.004
ORP-0.3100.029-0.1710.046-0.014-0.013-0.0081.0000.1860.004
Temp0.0800.311-0.516-0.176-0.017-0.035-0.0110.1861.0000.025
TEMP0.009-0.001-0.0020.002-0.0030.006-0.0040.0040.0251.000

Missing values

2023-03-05T11:38:14.440656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-05T11:38:14.750155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-05T11:38:15.157810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

pHECDOTSSTNTPTOCORPTempTEMP
Time
2017-07-11 14:00:007.3072731000.00.0136.2918183.70249086.42669720.298310250.74272732.43727329.743947
2017-07-11 15:00:007.3783331000.00.0139.2225003.98310387.74694317.942294249.35500032.75333328.987832
2017-07-11 16:00:007.3191671000.00.0134.5675004.01890282.20503717.774147255.73833332.46166731.228486
2017-07-11 17:00:007.3541671000.00.0141.1508333.33516664.42856917.437113239.02000032.58750029.483500
2017-07-11 18:00:007.2750001000.00.0134.4200002.79662351.73132619.871312255.34333332.16333329.167170
2017-07-11 19:00:007.3116671000.00.0130.9650003.92390757.21191117.359625249.27333331.39250028.162208
2017-07-11 20:00:007.3416671000.00.0128.3083333.87043874.36674922.503985249.90916730.80666728.281226
2017-07-11 21:00:007.3833331000.00.0126.4975002.69713873.61151019.270923245.71833330.29416728.887507
2017-07-11 22:00:007.4325001000.00.0124.5966673.56508672.78452517.595593245.40916729.82083330.055453
2017-07-11 23:00:007.4775001000.00.0122.8150003.09271669.87964521.069373244.57833329.39583329.142884
pHECDOTSSTNTPTOCORPTempTEMP
Time
2020-10-01 16:00:0012.3116676.2500008.144167147.8343183.23553783.62580913.091333387.31833326.35666730.520572
2020-10-01 17:00:0012.3533336.3800008.167500157.1524263.86640158.88410621.948128387.89666726.19250029.743345
2020-10-01 18:00:0012.3816679.1633338.189167194.2966484.65061287.64036718.729940387.87000026.37750027.760309
2020-10-01 19:00:0012.4000009.3366678.199167152.8426213.47296171.41760919.723982387.48833326.01500028.901547
2020-10-01 20:00:0012.5275006.8583338.177500139.7691172.77547166.97194621.204772385.91666725.95416729.642763
2020-10-01 21:00:0012.6341676.9891678.171667146.6953443.79202779.05613510.661287384.36583325.94333332.258008
2020-10-01 22:00:0012.40000010.4633338.300833159.6448683.93748371.72484816.870520387.44333324.95333330.020286
2020-10-01 23:00:0012.75416711.3733338.321667145.6195033.76450664.88237421.199508385.91250024.38250028.798577
2020-10-02 00:00:0012.7941679.3800008.284167128.5112524.44580671.55513720.153053384.05916724.70833331.503328
2020-10-02 01:00:0012.8000009.3800008.280000168.4043212.81432126.74428627.944556384.23000024.76000038.210078